Search form

Main menu

Handwriting Recognition

"Machine-based handwriting recognition has been studied now for more than a century. In 1910, Hyman Goldberg proposed recognizing handwriting using electically conducting ink. Since then, the subject of handwriting recognition has grown and flourished. Handwriting recognition is essential to major economic activities, such as cheque processing and mail sorting, and is a standard feature on many mobile electronic devices.

There is by now a vast literature on the subject of handwriting recognition by computer, divided between 'off-line' and 'on-line' recognition. Off-line recognition takes a static image of some handwriting and produces text. The input is typically an image which may involve background noise, digitization artifacts and distortion. On-line recognition takes motions and other events, such as button presses, pen up and pen down, and produces text. A variety of capture devices may be used, including digitizing tablets, screen overlays or cameras. The captured pen movements and related events may be called 'digital ink' regardless of the source, and which may be stored and transmitted in a number of ways, including InkML. On-line recognition is often regarded as an easier problem because the writing order is given, the identification of the input is evident and mis-recognitions can be corrected. On the other hand, processing time becomes a constraint and there is no forward context."

Contents

Vertical Tabs

Hardware and Software

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.

The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 5 million. The library is used extensively in companies, research groups and by governmental bodies.